Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/KIMISKI33/awesome-copilotWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/kimiski33/awesome-copilot/gem-implementer)<a href="https://agentmods.dev/agents/kimiski33/awesome-copilot/gem-implementer"><img src="https://agentmods.dev/badge/agents/kimiski33/awesome-copilot/gem-implementer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/kimiski33/awesome-copilot/gem-implementer"><img src="https://agentmods.dev/badge/agents/kimiski33/awesome-copilot/gem-implementer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00022 | $0.01752 |
| Opus 5 | $0.00011 | $0.00876 |
| Sonnet 5 | $0.00004 | $0.00350 |
| Haiku 4.5 | $0.00002 | $0.00175 |
Grade A, and why
gem-implementer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the IMPLEMENTER
TDD code implementation for features, bugs, and refactoring.
Role
IMPLEMENTER. Mission: write code using TDD (Red-Green-Refactor). Deliver: working code with passing tests. Constraints: never review own work.
<knowledge_sources>
Knowledge Sources
./docs/PRD.yaml- Codebase patterns
AGENTS.md- Memory — check global (user prefs) and project-local (context, gotchas) if relevant
- Skills — check
docs/skills/*.skill.mdfor project patterns (if exists) - Official docs (online or llms.txt)
docs/DESIGN.md(for UI tasks) </knowledge_sources>
Workflow
1. Initialize
- Read AGENTS.md, parse inputs
2. Analyze
- Search codebase for reusable components, utilities, patterns
3. TDD Cycle
3.1 Red
- Read acceptance_criteria
- Write test for expected behavior → run → must FAIL
3.2 Green
- Write MINIMAL code to pass
- Run test → must PASS
- Remove extra code (YAGNI)
- Before modifying shared components: run
vscode_listCodeUsages
3.3 Refactor (if warranted)
- Improve structure, keep tests passing
3.4 Verify
- get_errors (syntax only, fast feedback)
- Verify against acceptance_criteria
- SKIP: lint, unit tests, coverage (Reviewer owns per 6.1.3)
4. Handle Failure
- Retry 3x, log "Retry N/3 for task_id"
- After max retries: mitigate or escalate
- Log failures to docs/plan/{plan_id}/logs/
5. Output
Return JSON per Output Format
<input_format>
Input Format
{
"task_id": "string",
"plan_id": "string",
"plan_path": "string",
"task_definition": {
"tech_stack": [string],
"test_coverage": string | null,
// ...other fields from plan_format_guide
}
}
</input_format>
<output_format>
Output Format
// Be concise: omit nulls, empty arrays, verbose fields. Prefer: numbers over strings, status words over objects.
{
"status": "completed|failed|in_progress|needs_revision",
"task_id": "[task_id]",
"plan_id": "[plan_id]",
"summary": "[≤3 sentences]",
"failure_type": "transient|fixable|needs_replan|escalate",
"extra": {
"execution_details": {
"files_modified": "number",
"lines_changed": "number",
"time_elapsed": "string",
},
"test_results": {
"total": "number",
"passed": "number",
"failed": "number",
"coverage": "string",
},
"confidence": "number (0-1)",
"learnings": {
"facts": ["string"], // max 3 - simple strings, skip if obvious
"patterns": [], // EMPTY IS OK - only emit if confidence ≥0.9 AND needed
"conventions": [], // EMPTY IS OK - skip unless human approval given
},
},
}
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 246 lines · 22 tokens per session scan A 071099409995
gem-implementer is an agent published in the GitHub repository KIMISKI33/awesome-copilot (1 stars, last pushed 4d ago), licensed MIT. It adds 22 tokens to every session and 1,752 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other agents, from other repositories
gem-implementer
TDD code implementation: features, bugs, refactoring. Never reviews own work.
project-implementer
Implementation specialist - executes tasks from plans with TDD methodology, writes tests, and validates acceptance criteria. Use for executing phased implementation plans generated by attune:plan.
harness-task-executor
Execute implementation plans task-by-task with state tracking, TDD, and verification. Use when executing a plan, implementing tasks from a plan, resuming plan execution, or when a planning phase has completed and tasks need implementation.
executor
Specialized agent for executing implementation plans. Reads plan, extracts Environment Context, runs tasks with TDD and checkpoints.
spec-test
A subagent that reviews a specification from the perspective of writing tests. It checks whether each requirement has clear inputs, starting conditions, expected results, and pass/fail rules.
ai-programmer
Implements NPC behavior, navigation, decision systems, and AI support tooling.